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Abstract:
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Technologies such as mass spectrometry and two-dimensional gel electrophoresis are powerful tools for detecting protein changes potentially associated with disease. The associated data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Emphasis is usually placed on particular image features as a means to reduce data size and structure while retaining key information. Many researchers have proposed feature detection algorithms for such data, yet this remains an open problem due to the subsequent impact of existing approaches. This presentation will compare existing and newly developed algorithms for addressing this crucial stage of low-level analysis.
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